Yang Xue, Lauzon Carolyn B, Crainiceanu Ciprian, Caffo Brian, Resnick Susan M, Landman Bennett A
Electrical Engineering, Vanderbilt University, Nasvhille TN, 37235 USA.
Department of Biostatistics, Johns Hopkins University, Baltimore MD, 21205 USA.
Multimodal Brain Image Anal (2011). 2011;7012:1-9. doi: 10.1007/978-3-642-24446-9_1.
Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.
大规模单变量回归以及统计参数映射形式的推断改变了多维成像数据的研究方式。在功能和结构神经成像中，基于标准“设计矩阵”的一般线性回归模型及其多级同类模型使得对人类大脑生物学基础的研究成为可能。借助现代研究设计，可以获取同一个体的多个三维评估——例如，结构、功能和定量磁共振成像，以及正电子发射断层扫描的功能和配体结合图谱。当前的统计方法假定回归变量是非随机的。对于更现实的多参数评估（例如，体素级建模），考虑所有观测值的分布是合适的（例如，II型回归）。在此，我们描述一种使用设计矩阵范式的统一回归和推断方法，该方法兼顾了随机和非随机成像回归变量。
